Doc2Harness Benchmarks LLMs on Long‑Horizon Harness Engineering for Coding Agents
agents benchmarks
| Source: HF Papers | Original article
A new benchmark called Doc2Harness evaluates how large language model-powered coding agents perform on long‑horizon tasks, focusing on the surrounding harness as well as the model.
**Doc2Harness Introduces First Benchmark for Long‑Horizon Harness Engineering of Coding Agents**
Researchers from the Institute of Computing Technology, Chinese Academy of Sciences, Tsinghua University and Peking University have unveiled *Doc2Harness*, a new deterministic benchmark that evaluates large language models (LLMs) on their ability to construct complete “harnesses” for coding agents. Unlike existing tests that assess only downstream task success under a fixed harness, Doc2Harness asks the model to build the entire execution environment from scratch.
The benchmark supplies each model with a development document and a skeletal code base that declares only the required interfaces. The model must then generate a full harness, which is frozen and run as a black box through 328 observable test cases. Performance is measured by the harness’s behavior, not by any self‑reported output. In the initial run, 19 models produced 57 scored builds, with the highest pass rate still modest, highlighting the difficulty of the task.
Why it matters: As LLM‑driven agents move from isolated prompts to long‑horizon workflows—using tools, modifying files, and interacting with real‑world systems—the surrounding infrastructure (the “harness”) becomes as critical as the model itself. A reliable way to benchmark harness engineering exposes a blind spot in current evaluation practices and gives developers a concrete metric for improving agent reliability and safety.
What to watch next: The release is likely to spur a wave of research focused on harness design, automated environment generation, and integration testing for AI agents. Expect follow‑up studies that compare different model families, explore scaling effects, and propose new training objectives aimed at harness competence. Industry players that already ship agent‑centric products—such as Zenzap’s workplace messenger, Floqer’s sales‑prospecting agents, and Amp’s OpenAI‑powered tooling—may adopt Doc2Harness to validate and differentiate their solutions. The benchmark could also become a standard component of future AI agent competitions and open‑source toolkits, shaping how the Nordic AI community measures and builds trustworthy, long‑running agents.
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